Machine Learning Specialist

Location

L'Hospitalet de Llobregat, Barcelona | Spain

Job description

Job Description

Start Date: Immediately

Employment type: Full-time 

Location: L´Hospitalet de Llobregat, Spain 

Salary Range: from 40,000€ to 45,000€ annually. The precise salary will be determined based on experience.

Company Description:

Libre Foods is a pioneering biotech startup dedicated to revolutionizing the food industry by producing innovative food products derived from fungi. We are committed to sustainability, quality, and pushing the boundaries of food technology.

We are seeking a highly skilled Machine Learning Specialist to join our dynamic team at Libre Foods. Libre Foods is currently gathering a lot of raw data from various fermentation experiments. The rapid generation of data is facilitated by our own high-throughput screening platform, meticulously crafted for fermentation optimization purposes. In light of this, we are actively seeking a seasoned specialist to meticulously process this raw data and adeptly construct models aimed at extracting maximum insights. Furthermore, the specialist will be instrumental in devising strategies to curtail the requisite experimentation through the utilization of these models.

Key Responsibilities:

As a Machine Learning Specialist tasked with working on multifactorial data to improve mycelium growth and products thereof, your key responsibilities would include:

• Data Collection and Preparation: This could include data on environmental conditions (temperature, humidity, pH), nutrient composition, growth medium properties, and growth rates of mycelium. Prepare the data for analysis, ensuring it is clean, organized, and formatted appropriately for machine learning algorithms.
• Feature Engineering: Identify and engineer relevant features from the collected data that could potentially impact mycelium growth or the products thereof. This might involve transforming variables, creating interaction terms, or extracting meaningful features from raw data.
• Model Selection and Development: Choose appropriate machine learning algorithms and techniques to build predictive models based on the data. This could include regression models, decision trees, random forests, neural networks, or other advanced techniques. Experiment with different models to find the best fit for the data and the problem at hand.
• Model Training and Validation: Train machine learning models using historical data, and validate their performance using appropriate evaluation metrics. Employ techniques such as cross-validation to ensure the model's generalization capability and robustness.
• Model Interpretation: Interpret the results of machine learning models to understand the factors influencing mycelium growth. Identify important features and relationships between variables that can provide insights into optimizing growth conditions.
• Bioprocess Optimization: Use the insights gained from the models to optimize growth conditions for mycelium. This might involve adjusting environmental factors, nutrient compositions, or other parameters to maximize growth rates or improve the quality of the mycelium.
• Data Presentation: Create comprehensive reports detailing the findings from the machine learning analysis. These reports should be designed to facilitate easy understanding and accessibility of information for other team members. Utilize visualizations, charts, and graphs to effectively communicate complex concepts and insights derived from the data analysis.
• Documentation: Document the entire machine learning pipeline including data preprocessing steps, model development, validation results, and insights obtained. 
• Continuous Improvement: Continuously monitor and refine the machine learning models based on new data and insights. Stay updated with the latest advancements in machine learning techniques and apply them to enhance model performance and efficiency.

Must-Have:

• Bachelor's Degree (Master's preferred): Degree in Computer Science, Data Science, Engineering, or a related field. A Master's degree in Machine Learning, Artificial Intelligence, or a relevant discipline is preferred.
• Data Analysis Proficiency: Proficiency in data analysis techniques and experience with machine learning tools and frameworks such as Python (with libraries like TensorFlow, PyTorch, scikit-learn) or R. Ability to analyze complex datasets, derive insights, and develop predictive models to optimize fermentation process.
• Leadership and Cross-Functional Collaboration: Ability to lead cross-functional teams effectively in a fast-paced startup environment. Experience collaborating with teams across different departments such as research and development, production, procurement, and logistics to implement machine learning solutions.
• Problem-Solving Abilities: Strong critical thinking and creative problem-solving skills to address challenges and optimize processes using machine learning approaches. Capacity to identify bottlenecks, inefficiencies, and areas for improvement in bioprocess through data-driven analysis.
• Quantitative Analytical Skills: Detail-oriented with strong quantitative analytical skills to evaluate performance metrics, KPIs, and financial implications on decisions. Ability to apply statistical methods and machine learning algorithms to solve complex problems.
• Adaptability and Resilience: Ability to thrive in a fast-paced startup environment with evolving priorities and challenges. Resilience to navigate uncertainties and adapt strategies to changing market conditions or operational requirements.
• Continuous Learning: Commitment to continuous learning and staying updated with advancements in machine learning techniques, algorithms, and technologies. Willingness to explore new methodologies and tools to enhance optimization efforts.

Great-to-Have:

• Previous experience in fermentation optimization and modeling.
• Passionate about the future of food. 
• Computer Vision: Understanding of computer vision for tasks like image classification or object detection.
• Statistical Inference: Proficiency in statistical inference methods for hypothesis testing, confidence intervals, and regression analysis to make data-driven decisions and validate model results.

Job tags

Salary

de 40000 a 45000 €/año